Genetic algorithms are a standard technique for optimization processes. They are increasingly being used for “subjective” optimization where the goal (or objective or cost function) is determined by a user making “I like it” decisions. Typically, most optimization processes involve more than one objective Thus, there is now a large body of work on Evolutionary Multi-Objective Optimization (EMOO).
One way to work with multiple objectives is to evolve populations to optimize each objective independently, and to then crossbreed between the optimized populations. In one approach, each crossbreed is bred from one parent from each independently optimized population, for example one parent from an A-optimized population and one parent from a B-optimized population in a two parent scenario.
In that approach, however, many of the crossbred children are likely to have none of the carefully bred characteristics of their parents. While this is not too important where both functions A and B are numeric, as the crossbreeds can quickly and automatically be evaluated and inappropriate ones eliminated, in optimization problems where either or both A or B are subjective functions, this pre-evaluation of crossbreeds requires human judgment and thus time and potentially inhibitive effort. It is therefore desirable to reduce the likelihood of crossbred children having none of the bred characteristics of their parents in such optimization problems.